Video object segmentation
has been a longstanding task in computer vision,
which involves segmenting the pixels (or edges) belonging to an image into groups (e.g. objects).
In recent literature [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [2]}, [3]},
two protocols have attracted increasing interest from the vision community,
namely, semi-supervised video object segmentation (semi-supervised VOS),
and unsupervised video object segmentation (unsupervised VOS).
The former aims to re-localize one or multiple targets that are specified in the first frame of a video with pixel-wise masks, and the latter considers automatically separating the object of interest (usually the most salient one)
from the background in a video sequence.
Despite being called unsupervised VOS,
in practice,
the popular methods to address such problems extensively rely on supervised training, for example,
by using two-stream networks [6]}, [7]}, [8]}, [9]} trained on large-scale external datasets.
As an alternative,
in this work, we consider a completely unsupervised problem,
where no manual annotation is used for training whatsoever.
